Efficient multivariate linear mixed model algorithms for genome-wide association studies

Nat Methods. 2014 Apr;11(4):407-9. doi: 10.1038/nmeth.2848. Epub 2014 Feb 16.


Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Genome*
  • Likelihood Functions
  • Linear Models*
  • Multivariate Analysis*
  • Polymorphism, Single Nucleotide / genetics*
  • Software